{"id":"W4312891993","doi":"10.1609/icaps.v32i1.19804","title":"Biased Exploration for Satisficing Heuristic Search","year":2022,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Satisficing; Incremental heuristic search; Heuristic; Mathematical optimization; Beam search; Best-first search; Node (physics); Computer science; Consistent heuristic; Null-move heuristic; Greedy algorithm; Mathematics; Search algorithm; Algorithm; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005488111,0.001034024,0.001164778,0.001552979,0.0008072682,0.001347911,0.001402737,0.001402148,0.002915689],"category_scores_gemma":[0.03190095,0.0007376974,0.001049259,0.001237497,0.002078076,0.002634304,0.001998861,0.001878581,0.0004421707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001789524,"about_ca_system_score_gemma":0.002416239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001759929,"about_ca_topic_score_gemma":0.002077808,"domain_scores_codex":[0.9964898,0.001962169,0.0001598463,0.0003691236,0.0007397415,0.0002791996],"domain_scores_gemma":[0.9812708,0.01499482,0.001074716,0.001536198,0.0007976902,0.0003257669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004893775,0.0001460928,0.005741876,0.000405485,0.0001749915,0.0001657781,0.0004099655,0.7379405,0.003931968,0.1338324,0.003079419,0.1136821],"study_design_scores_gemma":[0.00006955065,0.0001165112,0.0003659727,0.00006913931,0.00003461932,0.00008857739,0.00004524241,0.9094453,0.001766072,0.08633508,0.00164415,0.0000197453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05285422,0.001299789,0.9376327,0.0006551179,0.00006464413,0.0001682509,0.00008033729,0.0007685357,0.006476338],"genre_scores_gemma":[0.7504599,0.0005861433,0.2457788,0.0005206353,0.00008975851,0.0005536291,0.0001770788,0.0002622994,0.001571781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005488111,"threshold_uncertainty_score":0.02902424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08200595162074595,"score_gpt":0.3090906722865417,"score_spread":0.2270847206657958,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}